IBNLearner_inl.h

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 *   Copyright (c) 2005-2025 by                                             *
 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
 *       - Christophe GONZALES(_at_AMU)                                     *
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 *   SPDX-FileCopyrightText: Copyright 2005-2025                            *
 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
 *       - Christophe GONZALES(_at_AMU)                                     *
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#pragma once
 

 
// to help IDE parser
#include <agrum/base/graphs/undiGraph.h>
#include <agrum/BN/learning/BNLearnUtils/IBNLearner.h>
 
namespace gum::learning {
 
 
  // returns the row filter
  INLINE DBRowGeneratorParser& IBNLearner::Database::parser() { return *_parser_; }
 
  // returns the modalities of the variables
  INLINE const std::vector< std::size_t >& IBNLearner::Database::domainSizes() const {
    return _domain_sizes_;
  }
 
  // returns the names of the variables in the database
  INLINE const std::vector< std::string >& IBNLearner::Database::names() const {
    return _database_.variableNames();
  }

  INLINE void IBNLearner::Database::setDatabaseWeight(const double new_weight) {
    if (_database_.nbRows() == std::size_t(0)) return;
    const double weight = new_weight / double(_database_.nbRows());
    _database_.setAllRowsWeight(weight);
  }
 
  // returns the node id corresponding to a variable name
  INLINE NodeId IBNLearner::Database::idFromName(const std::string& var_name) const {
    try {
      const auto cols = _database_.columnsFromVariableName(var_name);
      return _nodeId2cols_.first(cols[0]);
    } catch (...) {
      GUM_ERROR(MissingVariableInDatabase,
                "Variable " << var_name << " could not be found in the database")
    }
  }
 
  // returns the variable name corresponding to a given node id
  INLINE const std::string& IBNLearner::Database::nameFromId(NodeId id) const {
    try {
      return _database_.variableName(_nodeId2cols_.second(id));
    } catch (...) {
      GUM_ERROR(MissingVariableInDatabase,
                "Variable of Id " << id << " could not be found in the database")
    }
  }

  INLINE const DatabaseTable& IBNLearner::Database::databaseTable() const { return _database_; }

  INLINE const std::vector< std::string >& IBNLearner::Database::missingSymbols() const {
    return _database_.missingSymbols();
  }

  INLINE const Bijection< NodeId, std::size_t >& IBNLearner::Database::nodeId2Columns() const {
    return _nodeId2cols_;
  }

  INLINE std::size_t IBNLearner::Database::nbRows() const { return _database_.nbRows(); }

  INLINE std::size_t IBNLearner::Database::size() const { return _database_.size(); }

  INLINE void IBNLearner::Database::setWeight(const std::size_t i, const double weight) {
    _database_.setWeight(i, weight);
  }

  INLINE double IBNLearner::Database::weight(const std::size_t i) const {
    return _database_.weight(i);
  }

  INLINE double IBNLearner::Database::weight() const { return _database_.weight(); }
 
  // ===========================================================================
 
  // returns the node id corresponding to a variable name
  INLINE NodeId IBNLearner::idFromName(const std::string& var_name) const {
    return scoreDatabase_.idFromName(var_name);
  }
 
  // returns the variable name corresponding to a given node id
  INLINE const std::string& IBNLearner::nameFromId(NodeId id) const {
    return scoreDatabase_.nameFromId(id);
  }

  INLINE void IBNLearner::setDatabaseWeight(const double new_weight) {
    scoreDatabase_.setDatabaseWeight(new_weight);
  }

  INLINE void IBNLearner::setRecordWeight(const std::size_t i, const double new_weight) {
    scoreDatabase_.setWeight(i, new_weight);
  }

  INLINE double IBNLearner::recordWeight(const std::size_t i) const {
    return scoreDatabase_.weight(i);
  }

  INLINE double IBNLearner::databaseWeight() const { return scoreDatabase_.weight(); }
 
  // sets an initial DAG structure
  INLINE void IBNLearner::setInitialDAG(const DAG& dag) { initialDag_ = dag; }
 
  INLINE DAG IBNLearner::initialDAG() { return initialDag_; }
 
  // indicate that we wish to use an AIC score
  INLINE void IBNLearner::useScoreAIC() {
    scoreType_ = ScoreType::AIC;
    checkScorePriorCompatibility();
  }
 
  // indicate that we wish to use a BD score
  INLINE void IBNLearner::useScoreBD() {
    scoreType_ = ScoreType::BD;
    checkScorePriorCompatibility();
  }
 
  // indicate that we wish to use a BDeu score
  INLINE void IBNLearner::useScoreBDeu() {
    scoreType_ = ScoreType::BDeu;
    checkScorePriorCompatibility();
  }
 
  // indicate that we wish to use a BIC score
  INLINE void IBNLearner::useScoreBIC() {
    scoreType_ = ScoreType::BIC;
    checkScorePriorCompatibility();
  }
 
  // indicate that we wish to use a K2 score
  INLINE void IBNLearner::useScoreK2() {
    scoreType_ = ScoreType::K2;
    checkScorePriorCompatibility();
  }
 
  // indicate that we wish to use a Log2Likelihood score
  INLINE void IBNLearner::useScoreLog2Likelihood() {
    scoreType_ = ScoreType::LOG2LIKELIHOOD;
    checkScorePriorCompatibility();
  }
 
  // sets the max indegree
  INLINE void IBNLearner::setMaxIndegree(Size max_indegree) {
    constraintIndegree_.setMaxIndegree(max_indegree);
  }
 
  // indicate that we wish to use MIIC with constraints
  INLINE void IBNLearner::useMIIC() { selectedAlgo_ = AlgoType::MIIC; }

  INLINE void IBNLearner::useNMLCorrection() {
    kmodeMiic_ = CorrectedMutualInformation::KModeTypes::NML;
  }

  INLINE void IBNLearner::useMDLCorrection() {
    kmodeMiic_ = CorrectedMutualInformation::KModeTypes::MDL;
  }

  INLINE void IBNLearner::useNoCorrection() {
    kmodeMiic_ = CorrectedMutualInformation::KModeTypes::NoCorr;
  }

  INLINE std::vector< Arc > IBNLearner::latentVariables() const {
    return algoMiic_.latentVariables();
  }
 
  // indicate that we wish to use a K2 algorithm
  INLINE void IBNLearner::useK2(const Sequence< NodeId >& order) {
    selectedAlgo_ = AlgoType::K2;
    algoK2_.setOrder(order);
  }
 
  // indicate that we wish to use a K2 algorithm
  INLINE void IBNLearner::useK2(const std::vector< NodeId >& order) {
    selectedAlgo_ = AlgoType::K2;
    algoK2_.setOrder(order);
  }
 
  // indicate that we wish to use a greedy hill climbing algorithm
  INLINE void IBNLearner::useGreedyHillClimbing() {
    selectedAlgo_ = AlgoType::GREEDY_HILL_CLIMBING;
  }
 
  // indicate that we wish to use a local search with tabu list
  INLINE void IBNLearner::useLocalSearchWithTabuList(Size tabu_size, Size nb_decrease) {
    selectedAlgo_        = AlgoType::LOCAL_SEARCH_WITH_TABU_LIST;
    nbDecreasingChanges_ = nb_decrease;
    constraintTabuList_.setTabuListSize(tabu_size);
    localSearchWithTabuList_.setMaxNbDecreasingChanges(nb_decrease);
  }

  INLINE void IBNLearner::useEM(const double epsilon, const double noise) {
    if (epsilon < 0.0)
      GUM_ERROR(OutOfBounds, "EM's min log-likelihood evolution rate must be non-negative");
    if ((noise < 0.0) || (noise > 1.0))
      GUM_ERROR(OutOfBounds, "EM's noise must belong to interval [0,1]");
    if (epsilon > 0) {
      useEM_ = true;
      dag2BN_.setMinEpsilonRate(epsilon);
      dag2BN_.setNoise(noise);
      noiseEM_ = noise;
    } else {
      useEM_ = false;   // epsilon == 0
    }
  }

  INLINE void IBNLearner::useEMWithRateCriterion(const double epsilon, const double noise) {
    if (epsilon <= 0.0)
      GUM_ERROR(OutOfBounds, "EM's min log-likelihood evolution rate must be positive");
    useEM(epsilon, noise);
  }

  INLINE void IBNLearner::useEMWithDiffCriterion(const double epsilon, const double noise) {
    if (epsilon <= 0.0)
      GUM_ERROR(OutOfBounds, "EM's min log-likelihood differences must be positive");
    if ((noise < 0.0) || (noise > 1.0))
      GUM_ERROR(OutOfBounds, "EM's noise must belong to interval [0,1]");
    useEM_ = true;
    dag2BN_.setEpsilon(epsilon);
    dag2BN_.setNoise(noise);
    noiseEM_ = noise;
  }

  INLINE void IBNLearner::forbidEM() { useEM_ = false; }

  INLINE bool IBNLearner::isUsingEM() const { return useEM_; }

  INLINE EMApproximationScheme& IBNLearner::EM() {
    if (useEM_) return dag2BN_;
    else GUM_ERROR(NotFound, "EM is currently forbidden. Please enable it with useEM()")
  }

  INLINE IApproximationSchemeConfiguration::ApproximationSchemeSTATE IBNLearner::EMState() const {
    if (useEM_) return dag2BN_.stateApproximationScheme();
    else return IApproximationSchemeConfiguration::ApproximationSchemeSTATE::Undefined;
  }

  INLINE std::string IBNLearner::EMStateMessage() const {
    if (useEM_) return dag2BN_.messageApproximationScheme();
    else return "EM is currently forbidden. Please enable it with useEM()";
  }
 
  INLINE bool IBNLearner::hasMissingValues() const {
    return scoreDatabase_.databaseTable().hasMissingValues();
  }
 
  // assign a set of forbidden edges
  INLINE void IBNLearner::setPossibleEdges(const EdgeSet& set) {
    constraintPossibleEdges_.setEdges(set);
  }
 
  // assign a set of forbidden edges from an UndiGraph
  INLINE void IBNLearner::setPossibleSkeleton(const gum::UndiGraph& g) {
    setPossibleEdges(g.edges());
  }
 
  // assign a new possible edge
  INLINE void IBNLearner::addPossibleEdge(const Edge& edge) {
    constraintPossibleEdges_.addEdge(edge);
  }
 
  // remove a forbidden edge
  INLINE void IBNLearner::erasePossibleEdge(const Edge& edge) {
    constraintPossibleEdges_.eraseEdge(edge);
  }
 
  // assign a new forbidden edge
  INLINE void IBNLearner::addPossibleEdge(const NodeId tail, const NodeId head) {
    addPossibleEdge(Edge(tail, head));
  }
 
  // remove a forbidden edge
  INLINE void IBNLearner::erasePossibleEdge(const NodeId tail, const NodeId head) {
    erasePossibleEdge(Edge(tail, head));
  }
 
  // assign a new forbidden edge
  INLINE void IBNLearner::addPossibleEdge(const std::string& tail, const std::string& head) {
    addPossibleEdge(Edge(idFromName(tail), idFromName(head)));
  }
 
  // remove a forbidden edge
  INLINE void IBNLearner::erasePossibleEdge(const std::string& tail, const std::string& head) {
    erasePossibleEdge(Edge(idFromName(tail), idFromName(head)));
  }
 
  // assign a set of forbidden arcs
  INLINE void IBNLearner::setForbiddenArcs(const ArcSet& set) {
    constraintForbiddenArcs_.setArcs(set);
  }
 
  // assign a new forbidden arc
  INLINE void IBNLearner::addForbiddenArc(const Arc& arc) { constraintForbiddenArcs_.addArc(arc); }
 
  // remove a forbidden arc
  INLINE void IBNLearner::eraseForbiddenArc(const Arc& arc) {
    constraintForbiddenArcs_.eraseArc(arc);
  }
 
  // assign a new forbidden arc
  INLINE void IBNLearner::addForbiddenArc(const NodeId tail, const NodeId head) {
    addForbiddenArc(Arc(tail, head));
  }
 
  // remove a forbidden arc
  INLINE void IBNLearner::eraseForbiddenArc(const NodeId tail, const NodeId head) {
    eraseForbiddenArc(Arc(tail, head));
  }
 
  // assign a new forbidden arc
  INLINE void IBNLearner::addForbiddenArc(const std::string& tail, const std::string& head) {
    addForbiddenArc(Arc(idFromName(tail), idFromName(head)));
  }
 
  // remove a forbidden arc
  INLINE void IBNLearner::eraseForbiddenArc(const std::string& tail, const std::string& head) {
    eraseForbiddenArc(Arc(idFromName(tail), idFromName(head)));
  }
 
  // assign a set of forbidden arcs
  INLINE void IBNLearner::setMandatoryArcs(const ArcSet& set) {
    constraintMandatoryArcs_.setArcs(set);
  }
 
  // assign a new forbidden arc
  INLINE void IBNLearner::addMandatoryArc(const Arc& arc) { constraintMandatoryArcs_.addArc(arc); }
 
  // remove a forbidden arc
  INLINE void IBNLearner::eraseMandatoryArc(const Arc& arc) {
    constraintMandatoryArcs_.eraseArc(arc);
  }
 
  INLINE void IBNLearner::addNoParentNode(NodeId node) { constraintNoParentNodes_.addNode(node); }
 
  INLINE void IBNLearner::addNoParentNode(const std::string& name) {
    addNoParentNode(idFromName(name));
  }
 
  INLINE void IBNLearner::eraseNoParentNode(NodeId node) {
    constraintNoParentNodes_.eraseNode(node);
  }
 
  INLINE void IBNLearner::eraseNoParentNode(const std::string& name) {
    eraseNoParentNode(idFromName(name));
  }
 
  INLINE void IBNLearner::addNoChildrenNode(NodeId node) {
    constraintNoChildrenNodes_.addNode(node);
  }
 
  INLINE void IBNLearner::addNoChildrenNode(const std::string& name) {
    addNoChildrenNode(idFromName(name));
  }
 
  INLINE void IBNLearner::eraseNoChildrenNode(NodeId node) {
    constraintNoChildrenNodes_.eraseNode(node);
  }
 
  INLINE void IBNLearner::eraseNoChildrenNode(const std::string& name) {
    eraseNoChildrenNode(idFromName(name));
  }
 
  // assign a new forbidden arc
  INLINE void IBNLearner::addMandatoryArc(const std::string& tail, const std::string& head) {
    addMandatoryArc(Arc(idFromName(tail), idFromName(head)));
  }
 
  // remove a forbidden arc
  INLINE void IBNLearner::eraseMandatoryArc(const std::string& tail, const std::string& head) {
    eraseMandatoryArc(Arc(idFromName(tail), idFromName(head)));
  }
 
  // assign a new forbidden arc
  INLINE void IBNLearner::addMandatoryArc(NodeId tail, NodeId head) {
    addMandatoryArc(Arc(tail, head));
  }
 
  // remove a forbidden arc
  INLINE void IBNLearner::eraseMandatoryArc(NodeId tail, NodeId head) {
    eraseMandatoryArc(Arc(tail, head));
  }
 
  // sets a partial order on the nodes
  INLINE void IBNLearner::setSliceOrder(const NodeProperty< NodeId >& slice_order) {
    constraintSliceOrder_ = StructuralConstraintSliceOrder(slice_order);
  }
 
  INLINE void IBNLearner::setSliceOrder(const std::vector< std::vector< std::string > >& slices) {
    NodeProperty< NodeId > slice_order;
    NodeId                 rank = 0;
    for (const auto& slice: slices) {
      for (const auto& name: slice) {
        slice_order.insert(idFromName(name), rank);
      }
      rank++;
    }
    setSliceOrder(slice_order);
  }
 
  // sets the prior weight
  INLINE void IBNLearner::_setPriorWeight_(double weight) {
    if (weight < 0) { GUM_ERROR(OutOfBounds, "the weight of the prior must be positive") }
 
    priorWeight_ = weight;
    checkScorePriorCompatibility();
  }
 
  // use the prior smoothing
  INLINE void IBNLearner::useNoPrior() {
    priorType_ = BNLearnerPriorType::NO_prior;
    checkScorePriorCompatibility();
  }
 
  // use the prior smoothing
  INLINE void IBNLearner::useSmoothingPrior(double weight) {
    if (weight < 0) { GUM_ERROR(OutOfBounds, "the weight of the prior must be positive") }
 
    priorType_ = BNLearnerPriorType::SMOOTHING;
    _setPriorWeight_(weight);
 
    checkScorePriorCompatibility();
  }
 
  // use the Dirichlet prior
  INLINE void IBNLearner::useDirichletPrior(const std::string& filename, double weight) {
    if (weight < 0) { GUM_ERROR(OutOfBounds, "the weight of the prior must be positive") }
 
    priorDbname_ = filename;
    priorType_   = BNLearnerPriorType::DIRICHLET_FROM_DATABASE;
    _setPriorWeight_(weight);
 
    checkScorePriorCompatibility();
  }
 
  // use the prior BDeu
  INLINE void IBNLearner::useBDeuPrior(double weight) {
    if (weight < 0) { GUM_ERROR(OutOfBounds, "the weight of the prior must be positive") }
 
    priorType_ = BNLearnerPriorType::BDEU;
    _setPriorWeight_(weight);
 
    checkScorePriorCompatibility();
  }
 
  // returns the type (as a string) of a given prior
  INLINE PriorType IBNLearner::getPriorType_() const {
    switch (priorType_) {
      using enum gum::learning::IBNLearner::BNLearnerPriorType;
      case NO_prior : return PriorType::NoPriorType;
      case SMOOTHING : return PriorType::SmoothingPriorType;
      case DIRICHLET_FROM_DATABASE :
      case DIRICHLET_FROM_BAYESNET : return PriorType::DirichletPriorType;
      case BDEU : return PriorType::BDeuPriorType;
      default :
        GUM_ERROR(OperationNotAllowed,
                  "IBNLearner getPriorType does "
                  "not support yet this prior")
    }
  }
 
  // returns the names of the variables in the database
  INLINE const std::vector< std::string >& IBNLearner::names() const {
    return scoreDatabase_.names();
  }
 
  // returns the modalities  of the variables in the database
  INLINE const std::vector< std::size_t >& IBNLearner::domainSizes() const {
    return scoreDatabase_.domainSizes();
  }
 
  // returns the modalities  of a variable in the database
  INLINE Size IBNLearner::domainSize(NodeId var) const { return scoreDatabase_.domainSizes()[var]; }
 
  // returns the modalities  of a variables in the database
  INLINE Size IBNLearner::domainSize(const std::string& var) const {
    return scoreDatabase_.domainSizes()[idFromName(var)];
  }

  INLINE const std::vector< std::pair< std::size_t, std::size_t > >&
               IBNLearner::databaseRanges() const {
    return ranges_;
  }

  INLINE void IBNLearner::clearDatabaseRanges() { ranges_.clear(); }

  INLINE const DatabaseTable& IBNLearner::database() const {
    return scoreDatabase_.databaseTable();
  }
 
  INLINE Size IBNLearner::nbCols() const { return scoreDatabase_.domainSizes().size(); }
 
  INLINE Size IBNLearner::nbRows() const { return scoreDatabase_.databaseTable().size(); }
 
  // sets the number max of threads that can be used
  INLINE void IBNLearner::setNumberOfThreads(Size nb) {
    ThreadNumberManager::setNumberOfThreads(nb);
    if (score_ != nullptr) score_->setNumberOfThreads(nb);
  }
 
  /* namespace learning */
}   // namespace gum::learning